The range of applications in which sensor networks can be deployed depends heavily on the ease with which sensor locations/orientations can be registered and the accuracy of this process. We present a scalable strategy for algorithmic network calibration using sensor measurements from non-cooperative objects. Specifically, we use recently developed separable likelihoods in order to scale with the number of sensors whilst capturing the overall uncertainties. We demonstrate the efficacy of our self-configuration solution using a real network of radar and lidar sensors for perimeter protection and compare the accuracy achieved to manual calibration.
|Title of host publication||2018 SPIE Defense and Security, Signal Processing, Sensor/Information Fusion, and Target Recognition|
|Pages||10646 - 10646 - 13|
|Publication status||Published - 27 Apr 2018|
|Event||SPIE Defense + Commerical Sensing 2018 - Gaylord Palms Resort & Convention Center, Orlando, United States|
Duration: 15 Apr 2018 → 19 Apr 2018
|Conference||SPIE Defense + Commerical Sensing 2018|
|Period||15/04/18 → 19/04/18|